Lane level traffic

ABSTRACT

Lane level traffic levels are determined based on traffic camera images. A controller aligns a three-dimensional map with a traffic camera view, and identifies multiple lanes in the traffic camera view based on lane delineations of the three-dimensional map. The controller calculates a traffic parameter based on the multiple lanes in image frames from the traffic camera view and provides a traffic graphic based on the traffic parameter.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/324,797, filed Jul. 7, 2014, the entire contents of which areincorporated herein by reference.

FIELD

The following disclosure relates to the calculation of a trafficparameter from a camera view, or more particularly, the calculation oflane level traffic parameters from the coordination of three dimensionalmap data with a camera view.

BACKGROUND

Traffic technology is the study of movement of vehicles on the roads.Analytical techniques may manage and track traffic information andderive travel times, guide driving behavior and optimize roadinfrastructure for cities. Traffic Message Channel (TMC) and othertraffic services deliver traffic information to customers. Trafficincidents and traffic flow are reported through broadcasts. Trafficdelays may be caused by one or more of congestion, construction,accidents, special events (e.g., concerts, sporting events, festivals),weather conditions (e.g., rain, snow, tornado), and so on.

The traffic data is collected from probes. The probes may be individualdrivers or detection equipment near the road. The individual drivers maytravel with tracking devices. The detection equipment may be a loopdetector in or near the road that detects passing vehicles. Both ofthese approaches are based on intermittent samples. The sampling ratesmay vary (e.g., 10 out of 100 vehicles are detected) but do not provideexact and accurate traffic information. Neither approach provides lanelevel traffic information.

SUMMARY

Lane level traffic levels are determined based on traffic camera images.A controller aligns a three-dimensional map with a traffic camera view,and identifies multiple lanes in the traffic camera view based on lanedelineations of the three-dimensional map. The controller calculates atraffic parameter based on the multiple lanes in image frames from thetraffic camera view and provides a traffic graphic based on the trafficparameter.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the present invention are described herein withreference to the following drawings.

FIG. 1 illustrates an example system for lane level trafficdesignations.

FIG. 2 illustrates example sequence for lane level traffic designations.

FIG. 3 illustrates an example of a camera view and coordinated trafficparameters.

FIG. 4 illustrates an example set of graphical traffic indicators.

FIG. 5 illustrates another example set of graphical traffic indicators.

FIG. 6A illustrates an example table for the graphical trafficindicator.

FIG. 6B illustrates an example table adjusting the region of interest inthe camera view.

FIG. 7 illustrates an exemplary server of the system of FIG. 1.

FIG. 8 illustrates example flowchart for the server of FIG. 7.

FIG. 9 illustrates an exemplary mobile device of the system of FIG. 1.

FIG. 10 illustrates an example flowchart for the mobile device of FIG.9.

DETAILED DESCRIPTION

A traffic camera is a video camera that is positioned to record ortransmit video data of a road and the vehicles traveling on the road.Traffic cameras may be useful to give a snapshot of the current trafficconditions of the road or to monitor for traffic conditions such ascongestion, construction, or accidents. Typically, a user views thevideo stream and makes a conclusion on traffic conditions based on thevisible traffic. However, no traffic data can be easily extracted fromthe video stream images. The following embodiments extract traffic datafrom the video stream and provide the traffic data in another typeoutput.

FIG. 1 illustrates an example system for lane level trafficdesignations. The system 120 includes a developer system 121, one ormore mobile devices 122, a workstation 128, and a network 127. Thesystem 120 may receive a video stream including traffic from videocamera 124. Additional, different, or fewer components may be provided.For example, many mobile devices 122 and/or workstations 128 connectwith the network 127. The developer system 121 includes a server 125 anda database 123. The developer system 121 may include computer systemsand networks of a system operator.

The traffic data is extracted from the video stream through acorrelation with three-dimensional map data. The three-dimensional mapdata may be a high definition map compiled from a point cloud or otheroptical distancing data (e.g., light distance and ranging (LIDAR) data).The three-dimensional map data may include the locations of paths andmore specifically, locations of individual lanes within the paths.

The server 125 may be configured to align three-dimensional map datawith a video stream from a traffic camera view. The map and the trafficview may be aligned based on at least one common point and scale factorfor the traffic view. That is, at least one point in the traffic viewhas a known location in the three-dimensional map. The point may be astatic point such as the location of the camera which may be measuredusing a positioning technique (e.g., global positioning system). Thepoint may be a detected point such as indicia in the view of the camerathat can be detected from the video (e.g., a survey marker or paintedsymbol on the road). The scale factor describes the relative size ofobjects in the traffic camera view, relating real world distances topixel distances in the traffic camera view. The scale factor may also bebased on the indicia in the view of the camera or based on the currentfocal length of the camera.

The server 125 is configured to identify multiple lanes in the trafficcamera view based on lane delineations of the three-dimensional map. Thelane delineations may be the geographic locations of individual laneswithin one or more paths. The server 125 determines frame locations inthe two-dimensional view of the traffic camera that correspond to thelocations of individual lanes. The frame locations may be pixelcoordinates (e.g., horizontal coordinate and vertical coordinate) forthe vertices of vertices of an area for each of the lanes.

The server 125 may be configured to calculate a traffic parameter basedon moving objects in the lanes in image frames from the traffic cameraview. The traffic parameter may be a speed of the vehicles or a count ofthe number of vehicles that are traveling in the frame locations in thetwo-dimensional view that corresponds to the individual lanes. Thetraffic parameter may be another metric derived from the count ofvehicles and time or distance such as the speed of the traffic (distanceper unit time), the flow of the traffic (the number of vehicles ofpassing a reference point per time), or the density of the traffic (thenumber of vehicles per unit length of the path).

The server 125 may provide the traffic parameter to the mobile device122 or the workstation 128. The traffic parameter may be in a trafficgraphic such as a color-coded indicator, a numeric readout, or ananimated object. The animated object may be a gauge (e.g., radial dialindicator), moving vehicle images, or moving arrows.

The mobile device 122 is a smart phone, a mobile phone, a personaldigital assistant (“PDA”), a tablet computer, a notebook computer, apersonal navigation device (“PND”), a portable navigation device, and/orany other known or later developed portable or mobile computing device.The mobile device 122 includes one or more detectors or sensors as apositioning system built or embedded into or within the interior of themobile device 122. The mobile device 122 receives location data forgeographic position from the positioning system.

The optional workstation 128 is a general purpose computer includingprogramming specialized for the following embodiments. For example, theworkstation 128 may receive user inputs for activating lane leveldesignations. The workstation 128 may receive user inputs for manuallydefining the speed ranges for the color, shading, or alphanumeric labelsfor traffic designations of the path segment.

The developer system 121, the workstation 128, and the mobile device 122are coupled with the network 127. The phrase “coupled with” is definedto mean directly connected to or indirectly connected through one ormore intermediate components. Such intermediate components may includehardware and/or software-based components. The computing resources maybe divided between the server 125 and the mobile device 122. In someembodiments, the server 125 performs a majority of the processing. Inother embodiments, the mobile device 122 or the workstation 128 performsa majority of the processing. In another example, the processing isdivided substantially evenly between the server 125 and the mobiledevice 122 or workstation 128.

FIG. 2 illustrates example sequence for lane level traffic designations.FIG. 2 illustrates not necessarily separate hardware or devices but ageneral sequence of events of the system of FIG. 1.

The three-dimensional map data 131 is aligned with the video stream 132of the traffic camera 124. The three-dimensional map data 131 includesthree-dimensional locations for objects. The objects may includebuildings, points of interest, and pathways. One or more of the pathwaysinclude multiple lanes that may be defined according to surfaces,accessors and portals. A surface represents a navigable surface such asa road or intersection. An accessor represents an end node of thepathway and consists of portals. A portal represents represent lanecenter line location at the accessor and may be used to deriveattributes such as lane count and lane width. Lines connecting accessorsand/or portals may include control points that define the curvature ofthe lane, and accordingly, the curvature of the pathway. Lane and roadboundaries are detected from the traffic camera images using edgedetection and are subsequently aligned with lane models in the mapthrough a sequence of transformation including rotation, scaling andtranslation.

The three-dimensional map data 131 may be aligned with the video stream132 using different techniques. The alignment may be based on the focallength or field of view setting of the camera, which specified a rangefor a focal distance of a camera, providing a scale for the relativesizes of objects in the camera view. The sizes of the lanes in thethree-dimensional map data 131 are scaled to the relative size in thecamera view based on the focal distance of the camera. The server 125may be configured to calculate distances to the lanes from thethree-dimensional map 131 and correlate between the focal distance andthe distances to the lanes to align the three-dimensional map 131 withthe traffic camera view.

In another example, the alignment may be based on the sizes of objectsin the video stream. One or more of the objects may have dimensions thatare known. For example, the width of the lanes may be previouslyprovided (e.g., by physically measuring the width). Other exampleobjects and known dimensions may include a height of a guard rail, asize of a street sign, a distance from the ground to a traffic light, ora length or width of a specific type of vehicle. The relative size ofthe object in the image is used to calculate the scale for aligning thevideo stream to the three-dimensional map data.

After the alignment is completed, the server 125 may determine how farthe traffic camera is to any point in the video stream and a region ofinterest in a frame of video stream that corresponds to any object inthe three-dimensional map data. The server 125 may calculate a distanceto a first lane and determine a location in the traffic camera view forthe first lane based on the viewable distance range of the camera andthe position of the camera. The server 125 may calculate similardistances for a second lane and any number of additional lanes.

The video stream 132 is augmented using computer vision techniques, asillustrated by the computer vision block 133. Example computer visiontechniques include object recognition, motion tracking, foregrounddetection, and background subtraction. In background subtraction andforeground detection the background and foreground of images areseparated. The foreground may be the part of the camera view nearer theobserver or video or nearer to the point of view of the video camera124, and the background may be the part of the camera view farther fromthe observer or farther from the point of view of the video camera 124.

Alternatively, the background and foreground may be defined according tothe objects in the video. The background may include non-moving orstatic objects, and the foreground may include objects that are movingin the video. Movement of objects may be determined based on acomparison between a current frame and a reference frame. The referenceframe (e.g., background frame or background model) may be manually setas an image including no moving objects. The reference frame may be setas the preceding frame (e.g., movement is determined from anyconsecutive pair of frames). The reference frame may be set as anaverage of multiple frames over time.

Background subtraction may distinguish between the background andforeground of the camera view using frame differencing, a mean filter,and/or a gaussian average. Frame differencing includes a pixel-by-pixelcomparison of intensity values between the reference frame and thecurrent frame. The mean filter average a predetermined number ofpreceding frames and subtracts the result from the current frame. Thegaussian average utilizes a probabilistic density function for apredetermined number of preceding frames. The probabilistic densityfunction includes a mean and a standard deviation that are configurableto determine the degree of a Euclidean distance between pixels in thecurrent frame and the reference frame.

From the augmented video stream 132, the number of vehicles is countedor the speed of traffic is measured, as illustrated by the trafficmeasurement block 134. The result of the computer vision technique suchas background subtraction may be the locations of moving objects in theimage. The moving objects may correspond to vehicles. The datarepresenting the moving objects may be filter to remove pedestrians,bicycles, birds and other extraneous mobbing objects that should not becounted as vehicles.

The data from the traffic measurement is used to generate a graphicaltraffic meter 135. The traffic meter may include a textual output thatdescribes the speed of traffic or the volume of traffic over a timeperiod. The traffic meter may include a graphical object such as aradial dial or a sliding bar that represents the speed of traffic or thevolume of traffic. The traffic meter may be added to a map including thethree-dimensional map data 131.

FIG. 3 illustrates an example video stream 140 of a camera view andcoordinated traffic parameters. The video stream 140 may be accessiblethrough an internet protocol (IP) address. The video stream 140 mayinclude captured images at a frame rate. In order to maintain anaccurate count of vehicles, the server 125 may determine whether theframe rate meets or exceeds a threshold value. Example threshold valuesmay be 20, 10, or 1 frame per second. If the video stream 140 is lessthan the threshold, vehicles may pass through the camera view withoutbeing included in one of the images. The frame rate may be a function ofthe type of camera or the bandwidth of the connection with the camera.When the video stream 140 falls below the threshold frame rate, thetraffic meter may be modified to indicate that the data may beinaccurate.

The server 125 may identify a region 141 in the video stream 140 thatdepicts multiple lanes. The region 141 may include a polygon for each ofthe lanes. The polygons may be defined by the locations of the lanes inthe three-dimensional map data, but may also be modified as a functionof traffic speed, the frame rate of the video or the type of road. Theserver 125 may maintain a separate vehicle count for each of thepolygons.

An example map 142 may include lane level statistics based on thevehicle measurement. A circle 143 illustrates the portion of the map 142that corresponds to the region 141 in the video stream. The statisticsbased on the vehicle measurement may be shown using shadings 145 onindividual lanes. For example, a heavy or dark shading may indicate hightraffic or low speeds, and lighter shadings may indicate low or mediumtraffic or higher speeds. No shading may represent no traffic and normalspeeds. The map 142 may also include arrows indicative of the directionof travel on each of the lanes.

FIG. 4 illustrates an example set of graphical traffic indicators. Thegraphical traffic indicators may include arrows 157. The arrows 157 mayreflect traffic congestions on the road. The arrows 157 may be closertogether (short spacing distance) to indicate higher traffic and fartherapart (large spacing distance). The spacings of the arrows 157 may bedynamically adjusted as the rate of vehicles detected from the cameraview. The arrows 157 may be animated to move at a speed on the map thatis proportional or based on the traffic statistics of the lanes.

The lane level statistics may include traffic speed, traffic flow, ortraffic density. The graphical traffic indicators may include radialgauges (e.g., traffic speed gauge 151, traffic flow 153, and trafficdensity 155). Traffic speed may be calculated from two or more frames. Adistance between a location of an object in one of the frames and thelocation of the object in another frame divided by the time between theframes may provide traffic speed. The traffic speed may be a runningaverage over multiple successive pairs of image frames. The speeds ofmultiple vehicles may be averaged or otherwise combined to create aroadway speed. The roadway speed may be a running average over multiplesuccessive pairs of image frames.

Traffic flow may be calculated based on a number of vehicles that pass areference point over a time period. The number may be extrapolated tovehicles per hour or vehicles per minute. The inverse of flow, headway,provides the amount of time per passing vehicle, which may indicatevehicle spacing in a particular lane.

Traffic density may be calculated based on the number of vehicles perlength of the pathway. Traffic density may be directly measured (e.g.,identify a quantity of vehicles in the lanes of region 141) orindirectly based on the length of the pathway and the quantity ofvehicles. The server 125 may compare the traffic density to apredetermined critical density or traffic jam density. When the currenttraffic density exceed or is approaching the critical density, theserver 125 may generate a warning message that congestion is occurringor may occur.

FIG. 5 illustrates another example set of graphical traffic indicators.A graphical traffic indicator may be provided for each of multiplelanes. The example of FIG. 5 illustrates a north bound left laneindicator 161, a north bound right lane indicator 163, a south boundleft lane indicator 165, and a south bound right lane indicator 167. Theindicators may be based on a location specified by the user. Theindicators may be based on a current location of a navigation device ora future position of the navigation device. The future position may beidentified based on the current speed and direction of the navigationdevice or based on a route being followed by the navigation device. Theuser may make a routing decision based on the readouts of the graphicaltraffic indicators such as making a turn or exiting a freeway. The usermay make a lane change decision based on the relative readouts of themultiple lanes.

FIG. 6A illustrates an example table 150 for the graphical trafficindicator. The table 150 associates the position or readout of thegraphical traffic indicator with one or more of traffic speed, trafficflow, and traffic density. That is, traffic level may be defined basedon traffic speed, traffic flow, or traffic density. The traffic levelmay be assigned a numeric code. The example shown in table 150 includes5 numeric codes, each corresponding to a different traffic level (e.g.,1=very light traffic, 2=light traffic, 3=medium traffic, 4=heavytraffic, and 5=very heavy traffic). The traffic levels may be defined bytraffic speed such that each traffic level is associated with adifferent speed range (e.g., 5=0 to 5 mph, 4=6 to 10 mph, 3=11 to 20mph, 2=21 to 40 mph, and 1=41 to 60 mph or above). The traffic levelsmay be defined by traffic density such that each traffic level isassociated with a different density range (e.g., 1=0 to 1 vehicles perunit length, 2=1 to 2 vehicles per unit length, 3=2 to 3 vehicles perunit length, 4=3 to 4 vehicles per unit length, and 5=4 to 5 vehiclesper unit length). Example lengths include 10 feet, 10 meters, and 100feet. The traffic levels may be defined by traffic flow such that eachtraffic level is associated with a different traffic flow range (e.g.,1=above 50 vehicles passing the region per unit time, 2=21 to 50vehicles passing the region per unit time, 3=11 to 20 vehicles passingthe region per unit time, 4=6 to 10 vehicles passing the region per unittime, and 5=up to 5 vehicles passing the region per unit time). Exampleunit durations of time include 10 seconds, 30 seconds, and 1 minute. Thevarious traffic levels may be illustrated by the graphical trafficindicator using different needle positions, different colors, differentshadings, or different animation speeds.

In another embodiment, traffic level may be defined based on acombination of two or more of traffic speed, traffic flow, and trafficdensity. That is, each traffic level may be associated with a trafficspeed range and a traffic density range or another combination.

FIG. 6B illustrates an example table 155 adjusting the region ofinterest in the camera view. The server 125 may adjust the size of theregion of interest according to the type of path. The type of path maybe a functional classification. For example, for high speed roads (e.g.,highways or interstates), a large region of interest may be used becausevehicles are generally traveling faster than on a low speed road (e.g.,city streets), which uses a smaller region of interest. The size of theregion of interest may be set based on both functional classificationand traffic. For example, smaller sizes for the region of interest arereduced when the road is under congestion or higher traffic levels. Thearea of interest column in table 155 lists a coefficient thatcorresponds to the size of the area of interest (1 means 100% of thepossible size of the area of interest, 0.7 means 70% of the possiblesize of the area of interest, and so on).

One example of a simple system includes the functional classificationmaintained by the United States Federal Highway administration. Thesimple system includes arterial roads, collector roads, and local roads.The functional classifications of roads balance between accessibilityand speed. An arterial road has low accessibility but is the fastestmode of travel between two points. Arterial roads are typically used forlong distance travel. Collector roads connect arterial roads to localroads. Collector roads are more accessible and slower than arterialroads. Local roads are accessible to individual homes and business.Local roads are the most accessible and slowest type of road.

An example of a complex functional classification system is the urbanclassification system. Interstates include high speed and controlledaccess roads that span long distances. The arterial roads are dividedinto principle arteries and minor arteries according to size. Thecollector roads are divided into major collectors and minor collectorsaccording to size.

Another example functional classification system divides long distanceroads by type of road or the entity in control of the highway. Thefunctional classification system includes interstate expressways,federal highways, state highways, local highways, and local accessroads. Another functional classification system uses the highway tagsystem in the Open Street Map (OSM) system. The functionalclassification includes motorways, trunk roads, primary roads, secondaryroads, tertiary roads, and residential roads.

FIG. 7 illustrates an exemplary server 125 of the system of FIG. 1. Theserver 125 includes a processor 300, a communication interface 305, anda memory 301. The server 125 may be coupled to a database 123 and aworkstation 310. The workstation 310 may be used as an input device forthe server 125. In addition, the communication interface 305 is an inputdevice for the server 125. The communication interface 305 receives dataindicative of use inputs made via the workstation 128 or the mobiledevice 122. Additional, different, or fewer components may be included.FIG. 8 illustrates an example flowchart for lane level trafficdistinctions from video. The acts of FIG. 8 may be performed by theserver 125 or another device. Additional, different, or fewer acts maybe provided.

At act S101, the processor 300 is configured to align a traffic cameraview with map data including locations of multiple lanes of a road. Themap data may be stored by memory 301 or database 123. The communicationinterface 305 may receive location data relating to a current locationof a navigation device or a location selected through mappingapplication. The map data is a subset of data selected based on thelocation. The traffic camera view is a two-dimensional image frame orseries of image frames. The map data includes three-dimensionalcoordinates of geographical objects including the multiple lanes of theroad.

At act S103, the processor 300 is configured to identify multiple lanesin image frames of the traffic camera view based on the locations ofmultiple lanes in the map data. The processor 300 is configured toidentify pixels or pixel areas in the image frames that correspond tothe multiple lanes in the road. The processor 300 may extrapolatethree-dimensional locations in the map that correspond totwo-dimensional locations in the image based on at least one commonreference point and a viewable range. The common reference point mayinclude the installation location of the camera, and the viewable rangemay be based on the focal length of the camera.

At act S105, the processor 300 is configured to track vehicles in thetraffic camera view according to the multiple lanes in the image frames.The processor 300 may remove substantially all portions of the imageexcept the moving vehicles using background subtraction. The processor300 may track the vehicles in the lanes from a first video frame to asubsequent video frame. A count of vehicles or a speed of vehicles maybe based on a difference in the positions of the vehicles from the firstvideo frame to the subsequent video frame.

At act S107, the processor 300 is configured to calculate a trafficparameter based on vehicles in the image frames from the traffic cameraview. The traffic parameter is based on the count of vehicles and/or thespeed of the vehicles. The count of vehicles over time that pass apredetermined pixel distance in the image frame relates to a real worlddistance in the map data. The speed of vehicles may be average over timebased on relative locations of the same vehicles in adjacent imageframes.

The processor 300 may modify a display of the map including the multiplelanes based on the traffic parameter may. A moving indicator may beoverlaid on the road that indicates traffic level or an object may bedisplayed adjacent to the road, as discussed above.

FIG. 9 illustrates an exemplary mobile device 122 of the system ofFIG. 1. The mobile device 122 may be referred to as a navigation device.The mobile device 122 includes a controller 200, a memory 201, an inputdevice 203, a communication interface 205, position circuitry 207, and adisplay 211. The workstation 128 may include at least a memory andprocessor and may be substituted for the mobile device in the following.FIG. 10 illustrates an example flowchart for lane level trafficdetermination. The acts of FIG. 10 may be performed by the mobile device122 or another device. Additional, different, or fewer acts may beprovided.

At act S201, the position circuitry 207 or the controller 200 maydetermine location data indicative of a geographical location of amobile device. The geographic location may correspond a specific lane ofa multi-lane road or the road in general. At act S203, the communicationinterface 205 or the controller 200 sends the location data to a trafficserver. The traffic server determines traffic levels for the lane of themulti-lane road or multiple traffic levels for multiple lanes of themulti-lane road. The traffic levels are based on an analysis of imageframes of a video stream including the multi-lane road.

At act S205, the communication interface or the controller 200 receivesa traffic parameter from the traffic server. At act S207, the controller200 provides and/or the display 211 displays the traffic parameter inassociation with the lane of the multi-lane road. The traffic parametermay include traffic levels for the current lane that the mobile device122 is traveling in. The traffic parameter may include traffic levels inadjacent lanes that a driver may have the option to maneuver the vehicleinto. For example, the user may be shown upcoming traffic levels in onelane to the left of the current lane and one lane to the right of thecurrent lane. The user may make a lane change decision based on thetraffic parameter. The mobile device 122 may directly instruct the userto change lanes based on upcoming traffic levels.

The traffic parameters may also be used for autonomous driving. Inaddition or the alternative to traffic reporting, the traffic values maybe used to provide functions for an autonomous vehicle. An autonomousvehicle is self-driving and may be referred to as a robot vehicle or anautomated vehicle. The autonomous vehicle may include passengers but nodriver is necessary. The mobile device 122 or another computer system incommunication with the mobile device 122 may include instructions forrouting the vehicle or operating the vehicle. An estimated travel timemay be calculated based on the traffic values and a route may be chosenbased on the estimate travel time. The computing system may generatedriving commands for steering the vehicle, shifting gears, increasingand decreasing the throttle, and braking based on the traffic parameter.For example, when the traffic parameter indicates stop and go driving,the computing system of multiple autonomous vehicles may increase thespacing between vehicles to smooth out traffic congestion. The computingsystem may slow vehicles that are approaching congested areas. Thecomputing system may generate auxiliary commands for controlling theheadlights, turn signals, windshield wipers, defrost, or other auxiliaryfunctions not directly related to the movement of the vehicle.

The autonomous vehicle may include sensors for identifying thesurrounding and location of the car. The sensors may include GPS, lightdetection and ranging (LIDAR), radar, and cameras for computer vision.Proximity sensors may aid in parking the vehicle. The proximity sensorsmay detect the curb or adjacent vehicles. The autonomous vehicle mayoptically track and follow lane markings or guide markings on the road.

In addition, the server 125 may monitor user behavior in the imageframes to impact autonomous driving algorithms. The server 125 maydetermine how the average driver changes lanes. The server 125 maycompare a series of image frames to determine how long users typicallytake to move from one lane to another lane, the angle of movement fromone lane to another lane, and the speed or change in speed of vehiclesas they move from one lane to another lane. The server 125 may compare aseries of image frames to determine how users speed up or slow down, howmuch space they typically leave between vehicles, or how they adjustspeeds based on lane changes by other vehicles. The server 125 may applythese measurements to an autonomous driving algorithm in order to makethe autonomous control feel more like a human driver.

The database 123 may store or maintain geographic data such as, forexample, road segment or link data records and node data records. Thelink data records are links or segments representing the roads, streets,or paths. The node data records are end points (e.g., intersections)corresponding to the respective links or segments of the road segmentdata records. The road link data records and the node data records mayrepresent, for example, road networks used by vehicles, cars, and/orother entities. The road link data records may be associated withattributes of or about the roads such as, for example, geographiccoordinates, street names, address ranges, speed limits, turnrestrictions at intersections, and/or other navigation relatedattributes (e.g., one or more of the road segments is part of a highwayor tollway, the location of stop signs and/or stoplights along the roadsegments), as well as points of interest (POIs), such as gasolinestations, hotels, restaurants, museums, stadiums, offices, automobiledealerships, auto repair shops, buildings, stores, parks, etc. The nodedata records may be associated with attributes (e.g., about theintersections) such as, for example, geographic coordinates, streetnames, address ranges, speed limits, turn restrictions at intersections,and other navigation related attributes, as well as POIs such as, forexample, gasoline stations, hotels, restaurants, museums, stadiums,offices, automobile dealerships, auto repair shops, buildings, stores,parks, etc. The geographic data may additionally or alternativelyinclude other data records such as, for example, POI data records,topographical data records, cartographic data records, routing data, andmaneuver data.

The databases 123 may be maintained by one or more map developers (e.g.,the first company and/or the second company). A map developer collectsgeographic data to generate and enhance the database. There aredifferent ways used by the map developer to collect data. These waysinclude obtaining data from other sources such as municipalities orrespective geographic authorities. In addition, the map developer mayemploy field personnel (e.g., the employees at the first company and/orthe second company) to travel by vehicle along roads throughout thegeographic region to observe features and/or record information aboutthe features. Also, remote sensing such as, for example, aerial orsatellite photography may be used.

The database 123 may be master geographic databases stored in a formatthat facilitates updating, maintenance, and development. For example, amaster geographic database or data in the master geographic database isin an Oracle spatial format or other spatial format, such as fordevelopment or production purposes. The Oracle spatial format ordevelopment/production database may be compiled into a delivery formatsuch as a geographic data file (GDF) format. The data in the productionand/or delivery formats may be compiled or further compiled to formgeographic database products or databases that may be used in end usernavigation devices or systems.

For example, geographic data is compiled (such as into a physicalstorage format (PSF) format) to organize and/or configure the data forperforming navigation-related functions and/or services, such as routecalculation, route guidance, map display, speed calculation, distanceand travel time functions, and other functions, by a navigation device.The navigation-related functions may correspond to vehicle navigation,pedestrian navigation, or other types of navigation. The compilation toproduce the end user databases may be performed by a party or entityseparate from the map developer. For example, a customer of the mapdeveloper, such as a navigation device developer or other end userdevice developer, may perform compilation on a received geographicdatabase in a delivery format to produce one or more compiled navigationdatabases.

The input device 203 may be one or more buttons, keypad, keyboard,mouse, stylist pen, trackball, rocker switch, touch pad, voicerecognition circuit, or other device or component for inputting data tothe mobile device 122. The input device 203 and the display 211 may becombined as a touch screen, which may be capacitive or resistive. Thedisplay 211 may be a liquid crystal display (LCD) panel, light emittingdiode (LED) screen, thin film transistor screen, or another type ofdisplay.

The positioning circuitry 207 is optional and may be excluded for themap-related functions. The positioning circuitry 207 may include GPS,Global Navigation Satellite System (GLONASS), or a cellular or similarposition sensor for providing location data. The positioning system mayutilize GPS-type technology, a dead reckoning-type system, cellularlocation, or combinations of these or other systems. The positioningcircuitry 207 may include suitable sensing devices that measure thetraveling distance, speed, direction, and so on, of the mobile device122. The positioning system may also include a receiver and correlationchip to obtain a GPS signal. Alternatively or additionally, the one ormore detectors or sensors may include an accelerometer built or embeddedinto or within the interior of the mobile device 122. The accelerometeris operable to detect, recognize, or measure the rate of change oftranslational and/or rotational movement of the mobile device 122. Themobile device 122 receives location data from the positioning system.The location data indicates the location of the mobile device 122.

The controller 200 and/or processor 300 may include a general processor,digital signal processor, an application specific integrated circuit(ASIC), field programmable gate array (FPGA), analog circuit, digitalcircuit, combinations thereof, or other now known or later developedprocessor. The controller 200 and/or processor 300 may be a singledevice or combinations of devices, such as associated with a network,distributed processing, or cloud computing.

The memory 201 and/or memory 301 may be a volatile memory or anon-volatile memory. The memory 201 and/or memory 301 may include one ormore of a read only memory (ROM), random access memory (RAM), a flashmemory, an electronic erasable program read only memory (EEPROM), orother type of memory. The memory 201 and/or memory 301 may be removablefrom the mobile device 100, such as a secure digital (SD) memory card.

The communication interface 205 and/or communication interface 305 mayinclude any operable connection. An operable connection may be one inwhich signals, physical communications, and/or logical communicationsmay be sent and/or received. An operable connection may include aphysical interface, an electrical interface, and/or a data interface.The communication interface 205 and/or communication interface 305provides for wireless and/or wired communications in any now known orlater developed format.

The network 127 may include wired networks, wireless networks, orcombinations thereof. The wireless network may be a cellular telephonenetwork, an 802.11, 802.16, 802.20, or WiMax network. Further, thenetwork 127 may be a public network, such as the Internet, a privatenetwork, such as an intranet, or combinations thereof, and may utilize avariety of networking protocols now available or later developedincluding, but not limited to TCP/IP based networking protocols.

While the non-transitory computer-readable medium is shown to be asingle medium, the term “computer-readable medium” includes a singlemedium or multiple media, such as a centralized or distributed database,and/or associated caches and servers that store one or more sets ofinstructions. The term “computer-readable medium” shall also include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by a processor or that cause a computersystem to perform any one or more of the methods or operations disclosedherein.

In a particular non-limiting, exemplary embodiment, thecomputer-readable medium can include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. Further, the computer-readable medium can be arandom access memory or other volatile re-writable memory. Additionally,the computer-readable medium can include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an e-mail or other self-containedinformation archive or set of archives may be considered a distributionmedium that is a tangible storage medium. Accordingly, the disclosure isconsidered to include any one or more of a computer-readable medium or adistribution medium and other equivalents and successor media, in whichdata or instructions may be stored.

In an alternative embodiment, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, can be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments can broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that can be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system encompasses software, firmware, and hardwareimplementations.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedembodiment, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present specification describes components and functionsthat may be implemented in particular embodiments with reference toparticular standards and protocols, the invention is not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP,HTTPS) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a standalone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

As used in this application, the term ‘circuitry’ or ‘circuit’ refers toall of the following: (a) hardware-only circuit implementations (such asimplementations in only analog and/or digital circuitry) and (b) tocombinations of circuits and software (and/or firmware), such as (asapplicable): (i) to a combination of processor(s) or (ii) to portions ofprocessor(s)/software (including digital signal processor(s)), software,and memory(ies) that work together to cause an apparatus, such as amobile phone or server, to perform various functions) and (c) tocircuits, such as a microprocessor(s) or a portion of amicroprocessor(s), that require software or firmware for operation, evenif the software or firmware is not physically present.

This definition of ‘circuitry’ applies to all uses of this term in thisapplication, including in any claims. As a further example, as used inthis application, the term “circuitry” would also cover animplementation of merely a processor (or multiple processors) or portionof a processor and its (or their) accompanying software and/or firmware.The term “circuitry” would also cover, for example and if applicable tothe particular claim element, a baseband integrated circuit orapplications processor integrated circuit for a mobile phone or asimilar integrated circuit in server, a cellular network device, orother network device.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andanyone or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read only memory or arandom access memory or both. The essential elements of a computer are aprocessor for performing instructions and one or more memory devices forstoring instructions and data. Generally, a computer also includes, orbe operatively coupled to receive data from or transfer data to, orboth, one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio player, a Global Positioning System (GPS) receiver, to namejust a few. Computer readable media suitable for storing computerprogram instructions and data include all forms of non-volatile memory,media and memory devices, including by way of example semiconductormemory devices, e.g., EPROM, EEPROM, and flash memory devices; magneticdisks, e.g., internal hard disks or removable disks; magneto opticaldisks; and CD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a devicehaving a display, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information to the user and a keyboardand a pointing device, e.g., a mouse or a trackball, by which the usercan provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback, e.g., visualfeedback, auditory feedback, or tactile feedback; and input from theuser can be received in any form, including acoustic, speech, or tactileinput.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Additionally, the illustrations are merely representational and may notbe drawn to scale. Certain proportions within the illustrations may beexaggerated, while other proportions may be minimized. Accordingly, thedisclosure and the figures are to be regarded as illustrative ratherthan restrictive.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings and describedherein in a particular order, this should not be understood as requiringthat such operations be performed in the particular order shown or insequential order, or that all illustrated operations be performed, toachieve desirable results. In certain circumstances, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components in the embodiments described above should notbe understood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

One or more embodiments of the disclosure may be referred to herein,individually and/or collectively, by the term “invention” merely forconvenience and without intending to voluntarily limit the scope of thisapplication to any particular invention or inventive concept. Moreover,although specific embodiments have been illustrated and describedherein, it should be appreciated that any subsequent arrangementdesigned to achieve the same or similar purpose may be substituted forthe specific embodiments shown. This disclosure is intended to cover anyand all subsequent adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, are apparent to those of skill in the artupon reviewing the description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R.§1.72(b) and is submitted with the understanding that it will not beused to interpret or limit the scope or meaning of the claims. Inaddition, in the foregoing Detailed Description, various features may begrouped together or described in a single embodiment for the purpose ofstreamlining the disclosure. This disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter may be directed toless than all of the features of any of the disclosed embodiments. Thus,the following claims are incorporated into the Detailed Description,with each claim standing on its own as defining separately claimedsubject matter.

It is intended that the foregoing detailed description be regarded asillustrative rather than limiting and that it is understood that thefollowing claims including all equivalents are intended to define thescope of the invention. The claims should not be read as limited to thedescribed order or elements unless stated to that effect. Therefore, allembodiments that come within the scope and spirit of the followingclaims and equivalents thereto are claimed as the invention.

1. A method comprising: obtaining image frames from a camera having atraffic camera view of a multiple lane road; aligning, with a processor,a three-dimensional map with the traffic camera view of the multiplelane road, the three-dimensional map and the traffic camera view havingat least one common point with a known location; identifying a pluralityof lanes in the image frames of the traffic camera view based on lanedelineations of the three-dimensional map; tracking vehicles in theplurality of lanes between image frames of the traffic camera view; andcalculating a traffic parameter based on the vehicles in image frames ofthe traffic camera view.
 2. The method of claim 1, wherein aligning thethree-dimensional map with the traffic camera view comprises:calculating a focal distance of the camera for the traffic camera view;calculating a distance to the plurality of lanes from thethree-dimensional map; and determining a correlation between the focaldistance and the distance to the plurality of lanes to align thethree-dimensional map with the traffic camera view.
 3. The method ofclaim 2, wherein the correlation is based on a predetermined dimensionof an object in the traffic camera view, wherein the predetermineddimension of the object is a length of a car or a width of a path. 4.The method of claim 1, wherein the traffic parameter is based on adifference in the positions of the vehicles between image frames.
 5. Themethod of claim 1, wherein aligning the three-dimensional map with thetraffic camera view comprises: identifying a position of the camera forthe traffic camera view; identifying a viewable distance range of thecamera; calculating a first distance to a first lane of the plurality oflanes; and determining a first image location in the traffic camera viewfor the first lane based on the first distance and the viewable distancerange to the position of the camera.
 6. The method of claim 5, furthercomprising: calculating a second distance to a second lane of theplurality of lanes; and determining a second image location in thetraffic camera view for the second lane based on the second distance andthe viewable distance range to the position of the camera.
 7. The methodof claim 1, wherein the traffic parameter is traffic speed, trafficflow, or traffic density.
 8. The method of claim 1, wherein the trafficparameter includes data for each of the plurality of lanes.
 9. Themethod of claim 1, wherein the plurality of lanes travel in a samedirection and the traffic parameter includes data for each of theplurality of lanes in the same direction.
 10. The method of claim 1,further comprising one or more of: providing a traffic graphic based onthe traffic parameter; adding the traffic parameter to a user interfaceincluding the map data; and modifying an illustration of the road basedon the traffic parameter.
 11. The method of claim 1, further comprisingproviding the traffic parameter to a mobile device.
 12. An apparatuscomprising: at least one processor; and at least one memory includingcomputer program code for one or more programs; the at least one memoryand the computer program code configured to, with the at least oneprocessor, cause the apparatus to at least perform: align athree-dimensional map with a traffic camera view of a multiple laneroad, the three-dimensional map and the traffic camera view having atleast one common point with a known location; identify a plurality oflanes in image frames of the traffic camera view based on lanedelineations of the three-dimensional map; track vehicles in theplurality of lanes between image frames of the traffic camera view; andcalculate a traffic parameter based on the vehicles in image frames fromthe traffic camera view, wherein the plurality of lanes travel in a samedirection and the traffic parameter includes data for each of theplurality of lanes in the same direction.
 13. The apparatus of claim 12,wherein the traffic parameter is added to a user interface including mapdata.
 14. The apparatus of claim 12, wherein the traffic camera view isaligned with map data based on a focal distance of a camera and relativedistances of the multiple lanes.
 15. The apparatus of claim 12, whereinthe at least one memory and the computer program code are configured to,with the at least one processor, cause the apparatus to at leastperform: transmit the traffic parameter to a mobile device.
 16. Theapparatus of claim 12, wherein the traffic camera view is aligned withmap data based on a predetermined dimension of an object in the trafficcamera view.
 17. The apparatus of claim 12, wherein the trafficparameter is traffic speed, traffic flow, or traffic density.
 18. Theapparatus of claim 12, wherein the vehicles are counted in the imageframes using background subtraction.
 19. A non-transitory computerreadable medium including instructions that when executed on a computerare operable to: align a three-dimensional map with a traffic cameraview of a multiple lane road, the three-dimensional map and the trafficcamera view having at least one common point with a known location;identify a plurality of lanes in image frames of the traffic camera viewbased on lane delineations of the three-dimensional map; track vehiclesin the plurality of lanes between image frames of the traffic cameraview; and calculate a traffic parameter based on the vehicles in imageframes from the traffic camera view, wherein the plurality of lanestravel in a same direction and the traffic parameter includes data foreach of the plurality of lanes in the same direction.
 20. Thenon-transitory computer readable medium of claim 19 further includinginstructions that when executed on a computer are operable to: transmitthe traffic parameter to a mobile device.